Month: October 2018

You have probably heard a lot about artificial intelligence or “A.I.” if you have tech savvy friends. There was news this week that Canada was bidding on a new A.I. space arm for the unmanned moon orbiter that the U.S. is planning to launch in the next few years. The space arm needs to be able to learn simple tasks so that ground personnel can manage the orbiter by telling it to execute this or that rather than manipulating the arm through a complex series of movements. The IBM Watson project is another A.I. example where learning software tools optimize data collection and dissemination.

I raise this issue because A.I. software is driving many of the investment decisions for Wall Street hedge funds. A.I. software digests new information and makes trading decisions based on sophisticated algorithms. The problem is that it can have a major impact on your portfolio and the fallout can occur in a matter of minutes.

We have seen this play out over the past couple of weeks. Notable was the activity on Oct. 10 when the Dow Jones Industrial Average fell 800 points in one day. Much of the move occurred in the afternoon, when stocks appeared to fall off a cliff.

As expected, there was “insightful” spin on financial networks advancing reasonable cause and effect explanations. Market rotation was at the top of the list as analysts opined that managers were shifting focus from momentum (i.e. FANG) to value stocks (i.e. banks and utilities). Economists also weighed in, citing higher interest rates, trade tensions causing a sharp sell-off in Chinese markets, and more recently, concerns around the Italian budget deficit.

These are all reasonable arguments that, longer-term, will influence market trends. But the sell-off on Oct. 10 was more about A.I. then with any single motivating factor. You could see it during the day. Trades were triggered when the yield on the US ten-year Treasury note crossed 3.25%. There was a clear rotation away from FANG stocks (Facebook Amazon, Netflix and Alphabet (i.e. Google) and small caps with high debt to equity ratios into banks that benefit from higher rates.

The quick and sharp sell-off caused widespread angst, which carried over into virtually all sectors. At one point there were ten stocks down for every one that was up. For some perspective, the March 2009 post financial crisis bottom occurred at a point when there was a nine to one ratio of down to up stocks.

What makes me believe this was based on an algorithm was the fact that despite the Oct. 10 across the board sell-off we did not see significant capital movement away from stocks as an asset class into bonds and gold. To that point, I draw your attention to the accompanying chart showing the Dow Jones Industrial Average (the black bars) relative to the TLT (20-year Treasury bond ETF) and GLD (Gold bullion ETF). Notice when the Dow fell 800 points on Oct. 10, TLT and GLD barely moved. As the Dow fell another 400 points on Oct. 11, TLT moved higher by 0.8% and GLD was up about 2.5%. TLT and GLD have for the most part, remained at Oct. 11 levels.

I suspect the reaction on Oct. 11 was caused by the follow through in price action on the Dow. Investors were taking Wednesday’s move more seriously and wanted to shift some assets into safer havens. We saw a rebound on Friday, October 12, which, based on the action this week, seems to have been a bear market bounce.

This preamble sets the stage for the importance of staying focused through market turmoil. A.I. has simply shortened the time line of seismic shifts. It used to take weeks for traders to transition from bullish to bearish. It now happens in days and hours.

I’m not a big fan of money management via algorithms. It is not new and previous iterations have not benefitted investors. For example, the 1987 stock market crash was exacerbated by program trading, where algorithms systematically raised cash when specific price points were breeched. As the market fell through pre-determined levels, selling picked up. That caused further downside movement. The speed of the October 1987 sell-off caused wide spread panic and in the end did not protect investors in the way it was originally intended. Program trading went down as a failure and was pushed to the sidelines through much of the 1990s.

But in Wall Street, what goes around comes around. As a result, algorithms now account for as much as 30% of the trading activity on any given day. And this at a time when there are fewer shares to trade because of significant stock buybacks and institutional investors who follow a buy and hold approach (think Warren Buffett).

Make no mistake. NYSE and NASDAQ market makers who are charged with providing liquidity are keenly aware when they are taking the other side of a trade triggered by an algorithm. They also know that a machine is acting on electronic impulses and market makers will take advantage of that by shifting the bid and ask prices in accordance with the direction being taken by the algorithm. For the rest of us, that creates a trading pattern where we get sporadic volatility spikes interspersed with longer periods of narrow price swings.

It is virtually impossible to trade within these variables. I only hope that by understanding what is happening behind the scenes provides some comfort that will allow you to stick with an investment plan through turbulent times.

Longer term focus

If you believe, as I do, that intraday gyrations are more noise than substance, then you can focus on the factors that have real long-term implications. If institutional investors are shifting strategies from momentum to value, banks should benefit and FANG stocks should weaken.

It’s the same with the interest rate scenario. Higher rates should benefit banks, insurance companies, and large cap tech companies with sizeable cash hordes. Higher rates will be detrimental to small cap stocks because borrowing costs will rise. They will be particularly harmful to companies with significant leverage. I’m not sure that the current rate environment will have any major repercussions, but clearly the 3.25% rate on U.S. ten-year Treasury Notes is the current demarcation line.

But here’s the rub! Suppose we are correct about what is driving investment decisions. The point of our A.I. preamble is that much of what we think will happen has probably been priced into the market. The reaction time is simply too short to make moves after the fact, which means it is better to hedge your risks with a portfolio that can function within a multitude of detrimental scenarios.

Bottom line: Build an all-weather portfolio within your risk profile. Trying to trade the current environment will harm your pocketbook.